The Basics of Artificial Intelligence

And why it continues to be the hottest topic of 2016

Artificial Intelligence, also known as AI, was a hot topic in 2015 and will stay one in 2016.

Why?

Because business.

The commercial investment in AI in the last five years has exceeded the entire world wide government investment in AI research since it’s beginnings in the 1950's.

With Google opening up their findings in Machine Intelligence and algorithms finally catching up with the available hardware, the entire AI market will receive a big push. With those developments going on, it’s getting easier for small businesses to start thinking about the implementation of AI as well.

AI is a broad topic. It ranges from your phone’s calculator to self-driving cars to something in the future that might change the world dramatically. AI refers to all of these things, which is confusing.

We use AI all the time in our daily lives, but we often don’t realize it’s AI. John McCarthy, who coined the term “Artificial Intelligence” in 1956, complained that “as soon as it works, no one calls it AI anymore.”

In short: AI is the intelligenceexhibited by machines or software.

There are 3 main categories of AI:

Artificial Narrow Intelligence [ANI]: Also known as Weak AI. Specialises in one area or one task. It will be great at performing that task, but nothing more than that. Think of your computer opponent in online chess, smartphones, spam filters, etc.

Artificial General Intelligence [AGI]: Also known as Strong AI. Feels like a computer that is as smart as a human. A machine that can perform any intellectual task that a human being can.

Artificial Super Intelligence [ASI]: Much smarter than the best human brains in practically every field. Yup, that sounds as scary as it is. Even Elon Musk is afraid of this one.

Looking around you at the moment, you will realize we’ve mastered the skill of Artificial Narrow Intelligence pretty well by now. So, what’s next?

Biggest breakthroughs of 2015:

Abstracting across environmentsA long-term goal of the field of AI is to achieve artificial general intelligence, a single learning program that can learn and act in completely different situations at the same time, able to transfer some skills and knowledge learned. For example: making cookies and apply the knowledge later on to making brownies even better than it would have otherwise.

Intuitive concept understandingMore and more lines between the features of the data and the learned concepts will start to blur away. A demonstration of this shows a feature that can alter meaningful, automatic, photorealistic aspects of photographs, such as changing people’s facial expressions or their ages, or colorizing photos.

Creative abstract thoughtBeyond understanding simple concepts, lies understanding how ideas tie together to make things happen or tell a story in time. To be able to create things based on those understandings.

Building on the basic ideas from both DeepMind’s neural Turing machine and Facebook’s memory networks, combinations of deep learning and novel memory architectures have shown great promise in this direction this year.

Kumar and Socher’s dynamic memory network is able to read stories and answer questions about them, learning 20 kinds of reasoning, like deduction, induction, temporal reasoning, and path finding.

Dreaming up visionsThere are now AI’s able to ‘imagine’ meaningful new images as well. They do this not only by knowing about pattern recognition but pattern understanding and therefore also pattern creation.

A team from MIT and Microsoft Research have created a graphic network, which contains a special training technique to get neurons in its graphics code layer to change to meaningful transformations of an image. It makes them able to understand the 3D shapes in 2D images it receives, and able to photo-realistically imagine what it would be like to change things like camera angle and lighting.A massive gain for the image and video industry.

A team from NYU and Facebook found a way to generate realistic new images from meaningful and possible combinations of things it has seen in other images.

Earlier in the year, a German primatology team has recorded the hand motions of primates in combination with matching neural activity and they’re able to predict, based on brain activity, what fine motions are going on. They’ve also been able to teach those same fine motor skills to robotic hands, aiming at neural-enhanced prostheses.

Q: How long until computers have the same power as the human brain?A: 2025

This result may sound scary, but it will have a lot of benefits in the future as well. Mother Jones has this interesting theory:

“Imagine this; computers never get tired, they’re never grumpy, they never make mistakes, and they have instant access to all human knowledge. The result is paradise.”

Global warming is a problem of the past because computers have figured out how to generate limitless amounts of green energy and intelligent robots have tirelessly built the infrastructure to deliver it to our homes.

No one needs to work anymore. Robots can do everything humans can do, and they do it without complaining, 24 hours a day.

Here, here and here are some interesting articles on those developments.